Introduction The central problems of Linear Algebra are to study the properties of matrices and to investigate the solutions of systems of linear equations Given A and b, find x for Ax=b; i.e., solve Ax=b Given A, find λ (lambda) such that Ax=λ x

Matrices and Linear Systems of Equations Matrix notations and operations Elementary row operations Row-echelon form Matrix inverse LU-Decomposition Some special matrices

Determinants Definition of Determinant det(A) or |A| Cofactor and minor at (i,j)-position of A Properties of determinants Examples Applications

Vector Space and Linear Transform Vector space, subspace, Examples Null space, column space, row space of a matrix Solutions of m equations in n unknowns Spanning sets, linear independence, rank, basis, dimension Vector norms and matrix norms Linear transform Kernel and Image Projection, rotation, scaling Gauss transform Householder transform (elementary reflector) Jacobi transform (Givens’ rotation) * Affine transform is in general not a linear transform

Orthogonality Motivation – More intuitive Inner product and projection Orthogonal vectors and linear independence Orthogonal complement Projection and least squares approximation Orthonormal bases and orthogonal matrices Gram-Schmidt orthogonalization process QR factorization

Eigenvalues and Eigenvectors Definitions and Examples Properties of eigenvalues and eigenvectors Diagonalization of matrices A Markov process Differential equations and exp(A) Similarity transformation and triangularization Positive (Semi-definite) definite matrices Spectrum decomposition Singular Value Decomposition (SVD) Algebraic multiplicity and geometric multiplicity Minimal polynomials and Jordan blocks